Autonomous learning has been a promising direction in control and robotics for more than a decade since data-driven learning allows to reduce the amount of engineering knowledge, which is otherwise required. However, autonomous reinforcement learning (RL) approaches typically require many interactions with the system to learn controllers, which is a practical limitation in real systems, such as robots, where many interactions can be impractical and time consuming. To address this problem, current learning approaches typically require task-specific knowledge in form of expert demonstrations, realistic simulators, pre-shaped policies, or specific knowledge about the underlying dynamics. In this paper, we follow a different approach and speed up learning by extracting more information from data. In particular, we learn a probabilistic, non-parametric Gaussian process transition model of the system. By explicitly incorporating model uncertainty into long-term planning and controller learning our approach reduces the effects of model errors, a key problem in model-based learning. Compared to state-of-the art RL our model-based policy search method achieves an unprecedented speed of learning. We demonstrate its applicability to autonomous learning in real robot and control tasks.


    Access

    Download


    Export, share and cite



    Title :

    Gaussian Processes for Data-Efficient Learning in Robotics and Control


    Contributors:
    Deisenroth, MP (author) / Fox, D (author) / Rasmussen, CE (author)

    Publication date :

    2015-02-01


    Remarks:

    IEEE Transactions on Pattern Analysis and Machine Intelligence , 37 (2) pp. 408-423. (2015)


    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    DDC:    629 / 006



    Toward faster reinforcement learning for robotics applications by using Gaussian processes

    Younes, A. / Yushchenko, A. S. | American Institute of Physics | 2019


    Linear Gaussian Processes for Data-Efficient Robot Dynamics Learning

    Du, Desong / Liu, Changhao / Ni, Chenrui et al. | TIBKAT | 2022



    Linear Gaussian Processes for Data-Efficient Robot Dynamics Learning

    Du, Desong / Liu, Changhao / Ni, Chenrui et al. | Springer Verlag | 2021